Citation:

LOPEZ, R. D., M. S. NASH, D. T. HEGGEM, AND D. W. EBERT. USING LANDSCAPE ECOLOGY AND PARTIAL LEAST SQUARES PREDICTIONS TO MAP WATERSHEDS THAT ARE VULNERABLE TO NON-POINT SOURCE POLLUTION. Presented at Poster/Sigma Xi Conference, RTP, NC, November 02 - 05, 2006.

Impact/Purpose:

The primary objectives of this research are to:

Develop methodologies so that landscape indicator values generated from different sensors on different dates (but in the same areas) are comparable; differences in metric values result from landscape changes and not differences in the sensors;

Quantify relationships between landscape metrics generated from wall-to-wall spatial data and (1) specific parameters related to water resource conditions in different environmental settings across the US, including but not limited to nutrients, sediment, and benthic communities, and (2) multi-species habitat suitability;

Develop and validate multivariate models based on quantification studies;

Develop GIS/model assessment protocols and tools to characterize risk of nutrient and sediment TMDL exceedence;

Complete an initial draft (potentially web based) of a national landscape condition assessment.

This research directly supports long-term goals established in ORDs multiyear plans related to GPRA Goal 2 (Water) and GPRA Goal 4 (Healthy Communities and Ecosystems), although funding for this task comes from Goal 4. Relative to the GRPA Goal 2 multiyear plan, this research is intended to "provide tools to assess and diagnose impairment in aquatic systems and the sources of associated stressors." Relative to the Goal 4 Multiyear Plan this research is intended to (1) provide states and tribes with an ability to assess the condition of waterbodies in a scientifically defensible and representative way, while allowing for aggregation and assessment of trends at multiple scales, (2) assist Federal, State and Local managers in diagnosing the probable cause and forecasting future conditions in a scientifically defensible manner to protect and restore ecosystems, and (3) provide Federal, State and Local managers with a scientifically defensible way to assess current and future ecological conditions, and probable causes of impairments, and a way to evaluate alternative future management scenarios.

Description:

The U.S. Environmental Protection Agency's Office of Research and Development have mapped and interpreted landscape-scale (i.e., broad scale) ecological metrics among watersheds in the upper White River watershed, producing the first geospatial models of water quality vulnerability in the Ozark Mountains of Missouri and Arkansas (USA).These analyses utilized a combination of partial least squares (PLS), existing field water quality monitoring station data, remote sensing information, and a priori information about landscape conditions of the associated subwatershed(s). The analyses were conducted at multiple geographic scales, from the site-specific water quality measurements (fine-scale) to the broader-scale watershed analyses, which have been reported among 8-digit U.S. Geological Survey hydrologic units and 244 customized subwatersheds. The 244 subwatersheds were customized for this project to increase the precision and accuracy of water quality vulnerability predictions and were based on watershed terrain and a single "pour point" for each subwatershed where all runoff exits the watershed. Using PLS, we determined four different (surface) water quality conditions among the 244 customized subwatersheds of the Ozarks, which may be useful for important management decisions in the region: (1) subwatersheds that have high concentrations of total ammonia, high concentrations of total phosphorus, and high cell counts of Escherichia coli (E. coli); (2) subwatersheds that have high concentrations of total ammonia, low concentrations of total phosphorus, and high cell counts of E. coli; (3) subwatersheds that have low concentrations of total ammonia, low concentrations of total phosphorus, and high cell counts of E. coli; and (4) subwatersheds that have moderate concentrations of both total ammonia and total phosphorus and moderate E. coli cell counts. The results of this project provide watershed managers with the first broad-scale predictions that can be used to explain how land cover type, land cover configuration, environmental change, and human activities may affect the chemical and biological characteristics of surface water in the Upper White River region. The amount of variability in surface water constituents explained by each model reflects the composition of the contributing landscape metrics. The landscape-water model developed using PLS explains 59%, 93%, and 81% of the variation in surface water total phosphorous, total ammonia, and E. coli, respectively.

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